Predicting the content dissemination trends by repost behavior modeling in mobile social networks

Abstract

Internet of Things (IoT) invasions a future in which digital and physical entities (e.g., mobile devices, wearable devices) can be linked, by means of appropriate information and communication technologies, to enable a whole new class of applications and services. In this paper, we study the dissemination of content over a mobile social network, which has become an attractive proxy for investigating human behaviors due to the rapid development of mobile phones. One of the most interesting and challenging problems about content dissemination is that how much attention of a specific post from a user can ultimately gain? Hence, in other words, can we forecast the crowds׳ concern in the mobile social networking environment, and how? We try to tackle this issue by exploring approaches to predict the amount of reposts any given post will obtain in Sina Weibo, a well-known mobile social networking service in China. We examine several novel implicit factors impacting the popularity of content, such as Modality, MaxMediaWeight and Activeness. Furthermore, we propose a RepostsTree based method to model the reposting process in a temporal dynamic manner. Experimental results over the collected data from Sina Weibo indicate that our method is effective on content diffusion prediction in mobile social networks.